Current methods of providing insurance are based on statistical analysis of a multitude of risk factors related to a wide variety of personal and demographic information associated with an insured. While these methods have become more sophisticated over time, they still have their limitations. As an example, the cost of an automobile insurance policy is often based on the age and sex of the insured, their primary geographic location, as well as the class of their vehicle. Other factors, such as the number of miles the insured drives in a year, and the ratio of business to personal miles driven, is increasingly being taken into consideration. All of these risk factors are then typically compared to the accident incidence and claim rates of a pool of drivers that are the same sex and age group, drive the same class of vehicle, live in the same geographical area, and drive a similar number of miles every year. These comparisons generally provide useful information related to the frequency, and cost, of claims made by members of the insured pool. However, these approaches remain generalized and are not oriented to the travel behavior of a specific insured or insured vehicle.
For example, three individual insureds may drive the same type of vehicle, live in the same neighborhood, and have final destinations that are in close proximity to one another. Yet the first driver may typically take a route through neighborhoods with high crime rates in order to save transit time. The second driver, also hoping to save transit time, may prefer taking a highway route that avoids high crime areas but has a high percentage of rush hour traffic accidents. The third driver, being more cautious, may elect to take a slower, yet safer route. Despite the respective risk of each route, all three drivers may pay the same auto insurance premium for the same amount of coverage.
The use of telematics in automobiles has become more common in recent years, particularly as implemented with in-car navigation systems. Based upon provision of a current location and a desired destination, these systems typically provide an optimum route between two points. However, this routing is typically oriented towards the shortest route, which may not be the safest or the quickest. In recent years, real-time information feeds from satellite imaging, traffic control, law enforcement, and weather forecasting systems have become more available and suitable for integration. Current approaches are known for processing information from these and other sources to provide multiple routes, each with a corresponding risk index, between two points. In some cases, these routes may comprise a multitude of route segments, each with a corresponding risk index, which are used to provide a composite risk index for a route. The individual risk index for each segment, or the composite risk index for each route, is usually displayed within the user interface of a navigation system. The user then selects the route that most closely matches their individual tolerance for risk. However, insurance providers currently have no way of determining which route, or route segment, a driver may take or what driving behavior they exhibit along the route. Furthermore, insurance providers lack the information to either lower the cost of an automobile insurance policy in response to a driver selecting a route with a low risk index or driving safely, or alternatively, raise the cost of the policy for electing to follow a route with a high risk index or driving aggressively.